Since sharp jumps are frequently found in real-world data, making machine-learning models that can learn sharp jumps is a significant challenge in interpretable models [1]}, [2]}.
However, neural networks struggle to capture such sharp jumps [1]} because of the bias towards smooth functions [4]}, [5]}.
[1]} have proposed the exp centered (ExU) unit to address this problem.
However, the ExU unit merely works well under certain circumstances.
